In the last few years, three dimensional (3D) head tracking in a video sequence or relative pose estimation from multiple images has been recognized as an essential prerequisite for robust facial expression/emotion analysis, and synthesis of face recognition. Tracking 3D head pose is also an important component for correcting for eye-gaze in video-conferencing, by helping to achieve and preserve eye-contact between participants of a video conference in a desktop environment.
Most conventional 3D head pose systems can be characterized as suffering from one or more of the following drawbacks: (1) they are unable to capture head position images accurately; (2) they are limited in the amount range of head motions the systems can track; and/or (3) they require considerable amounts of processing making real-time applications impractical.